To use Sentence Transformers for plagiarism detection or finding similar documents, you would leverage their ability to generate dense vector representations (embeddings) of text that capture semantic meaning. The core idea is that documents with similar content will have embeddings that are close to each other in vector space. For example, a plagiarism detector could encode a submitted document and compare its embedding against a database of existing documents to find near-duplicates. Similarly, a document retrieval system could use similarity scores to identify related articles or reports.
First, you’d encode all reference documents (e.g., existing essays, articles, or code) into embeddings using a pre-trained Sentence Transformer model like all-mpnet-base-v2 or multi-qa-mpnet-base-dot-v1. These models are optimized for semantic similarity tasks. For large datasets, you’d store these embeddings in a vector database like FAISS or Annoy to enable fast similarity searches. When a new document is submitted, you’d encode it into an embedding and compute similarity scores (e.g., using cosine similarity) against the stored embeddings. Documents exceeding a predefined similarity threshold (e.g., 0.85) would be flagged for review. For example, a student essay with a 95% similarity score to a published paper would trigger a plagiarism alert.
Practical implementation involves handling edge cases. For instance, splitting long documents into paragraphs or sentences before encoding ensures the model processes text within its token limit. You might also fine-tune the model on domain-specific data (e.g., academic papers or legal contracts) to improve accuracy. Additionally, using techniques like BM25 alongside semantic similarity can help balance keyword overlap with contextual meaning. For scalability, batch processing and distributed computing frameworks (e.g., Apache Spark) can manage encoding millions of documents. Performance optimization might include quantizing embeddings to reduce memory usage or using GPU acceleration for faster inference. Testing with real-world data is critical to calibrate thresholds and minimize false positives.